MAINT: Fix CircleCI (mne-tools#13351)

Author: larsoner

mne/utils/misc.py

@@ -358,12 +358,27 @@ def _file_like(obj):
     return all(callable(getattr(obj, name, None)) for name in ("read", "seek"))
 
 
-def _fullname(obj):
+def _fullname(obj, *, referent=None):
     klass = obj.__class__
     module = klass.__module__
-    if module == "builtins":
-        return klass.__qualname__
-    return module + "." + klass.__qualname__
+    name = klass.__qualname__
+    if module != "builtins":
+        name = f"{module}.{name}"
+    if referent is not None:
+        if isinstance(obj, list | tuple):
+            for ii, item in enumerate(obj):
+                if item is referent:
+                    name += f"[{ii}]"
+                    break
+        elif isinstance(obj, dict):
+            for key, value in obj.items():
+                if key is referent:
+                    name += "-key"
+                    break
+                if value is referent:
+                    name += f"[{key!r}]"
+                    break
+    return name
 
 
 def _assert_no_instances(cls, when=""):
@@ -372,7 +387,7 @@ def _assert_no_instances(cls, when=""):
     ref = list()
     gc.collect()
     objs = gc.get_objects()
-    for obj in objs:
+    for obj in objs:  # e.g., vtkPolyData, Brain, Plotter, etc.
         try:
             check = isinstance(obj, cls)
         except Exception:  # such as a weakref
@@ -382,30 +397,33 @@ def _assert_no_instances(cls, when=""):
                 ref.append(f"Brain._cleaned = {getattr(obj, '_cleaned', None)}")
             rr = gc.get_referrers(obj)
             count = 0
-            for r in rr:
+            for r in rr:  # e.g., list, dict, etc. that holds the reference to obj
                 if (
                     r is not objs
                     and r is not globals()
                     and r is not locals()
                     and not inspect.isframe(r)
                 ):
+                    name = _fullname(r, referent=obj)
                     if isinstance(r, list | dict | tuple):
                         rep = f"len={len(r)}"
                         r_ = gc.get_referrers(r)
-                        types = (_fullname(x) for x in r_)
-                        types = "/".join(sorted(set(x for x in types if x is not None)))
-                        rep += f", {len(r_)} referrers: {types}"
+                        types = list()
+                        for x in r_:
+                            types.append(_fullname(x, referent=r))
+                        types = " / ".join(sorted(types))
+                        rep += f" | {len(r_)} referrers: {types}"
                         del r_
                     else:
-                        rep = repr(r)[:100].replace("\n", " ")
+                        rep = "repr="
+                        rep += repr(r)[:100].replace("\n", " ")
                         # If it's a __closure__, get more information
                         if rep.startswith("<cell at "):
                             try:
                                 rep += f" ({repr(r.cell_contents)[:100]})"
                             except Exception:
                                 pass
-                    name = _fullname(r)
-                    ref.append(f"{name}: {rep}")
+                    ref.append(f"{name} with {rep}")
                     count += 1
                 del r
             del rr

tutorials/preprocessing/70_fnirs_processing.py

@@ -30,7 +30,6 @@
 raw_intensity = mne.io.read_raw_nirx(fnirs_cw_amplitude_dir, verbose=True)
 raw_intensity.load_data()
 
-
 # %%
 # Providing more meaningful annotation information
 # ------------------------------------------------
@@ -48,7 +47,6 @@
 unwanted = np.nonzero(raw_intensity.annotations.description == "15.0")
 raw_intensity.annotations.delete(unwanted)
 
-
 # %%
 # Viewing location of sensors over brain surface
 # ----------------------------------------------
@@ -89,7 +87,6 @@
     n_channels=len(raw_intensity.ch_names), duration=500, show_scrollbars=False
 )
 
-
 # %%
 # Converting from raw intensity to optical density
 # ------------------------------------------------
@@ -99,7 +96,6 @@
 raw_od = mne.preprocessing.nirs.optical_density(raw_intensity)
 raw_od.plot(n_channels=len(raw_od.ch_names), duration=500, show_scrollbars=False)
 
-
 # %%
 # Evaluating the quality of the data
 # ----------------------------------
@@ -118,14 +114,12 @@
 ax.hist(sci)
 ax.set(xlabel="Scalp Coupling Index", ylabel="Count", xlim=[0, 1])
 
-
 # %%
 # In this example we will mark all channels with a SCI less than 0.5 as bad
 # (this dataset is quite clean, so no channels are marked as bad).
 
 raw_od.info["bads"] = list(compress(raw_od.ch_names, sci < 0.5))
 
-
 # %%
 # At this stage it is appropriate to inspect your data
 # (for instructions on how to use the interactive data visualisation tool
@@ -134,7 +128,6 @@
 # If your data contains lots of artifacts you may decide to apply
 # artifact reduction techniques as described in :ref:`ex-fnirs-artifacts`.
 
-
 # %%
 # Converting from optical density to haemoglobin
 # ----------------------------------------------
@@ -145,7 +138,6 @@
 raw_haemo = mne.preprocessing.nirs.beer_lambert_law(raw_od, ppf=0.1)
 raw_haemo.plot(n_channels=len(raw_haemo.ch_names), duration=500, show_scrollbars=False)
 
-
 # %%
 # Removing heart rate from signal
 # -------------------------------
@@ -178,7 +170,6 @@
 events, event_dict = mne.events_from_annotations(raw_haemo)
 fig = mne.viz.plot_events(events, event_id=event_dict, sfreq=raw_haemo.info["sfreq"])
 
-
 # %%
 # Next we define the range of our epochs, the rejection criteria,
 # baseline correction, and extract the epochs. We visualize the log of which
@@ -203,7 +194,6 @@
 )
 epochs.plot_drop_log()
 
-
 # %%
 # View consistency of responses across trials
 # -------------------------------------------
@@ -221,7 +211,6 @@
     ts_args=dict(ylim=dict(hbo=[-15, 15], hbr=[-15, 15])),
 )
 
-
 # %%
 # We can also view the epoched data for the control condition and observe
 # that it does not show the expected morphology.
@@ -233,7 +222,6 @@
     ts_args=dict(ylim=dict(hbo=[-15, 15], hbr=[-15, 15])),
 )
 
-
 # %%
 # View consistency of responses across channels
 # ---------------------------------------------
@@ -250,7 +238,6 @@
     for ax in axes[:, column]:
         ax.set_title(f"{condition}: {ax.get_title()}")
 
-
 # %%
 # Plot standard fNIRS response image
 # ----------------------------------
@@ -277,7 +264,6 @@
     evoked_dict, combine="mean", ci=0.95, colors=color_dict, styles=styles_dict
 )
 
-
 # %%
 # View topographic representation of activity
 # -------------------------------------------
@@ -290,7 +276,6 @@
     times=times, topomap_args=topomap_args
 )
 
-
 # %%
 # Compare tapping of left and right hands
 # ---------------------------------------